2019-09-05 15:29:41 +00:00
|
|
|
import random
|
|
|
|
|
|
2019-09-18 13:35:17 +00:00
|
|
|
import scipy.signal
|
2019-09-05 15:29:41 +00:00
|
|
|
import torch as th
|
|
|
|
|
import numpy as np
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def set_random_seed(seed, using_cuda=False):
|
|
|
|
|
"""
|
|
|
|
|
Seed the different random generators
|
|
|
|
|
:param seed: (int)
|
|
|
|
|
:param using_cuda: (bool)
|
|
|
|
|
"""
|
|
|
|
|
random.seed(seed)
|
|
|
|
|
np.random.seed(seed)
|
|
|
|
|
th.manual_seed(seed)
|
|
|
|
|
|
|
|
|
|
if using_cuda:
|
|
|
|
|
# Make CuDNN Determinist
|
|
|
|
|
th.backends.cudnn.deterministic = True
|
|
|
|
|
th.cuda.manual_seed(seed)
|
2019-09-18 13:35:17 +00:00
|
|
|
|
|
|
|
|
|
2019-09-19 09:43:27 +00:00
|
|
|
# From stable baselines
|
|
|
|
|
def explained_variance(y_pred, y_true):
|
|
|
|
|
"""
|
|
|
|
|
Computes fraction of variance that ypred explains about y.
|
|
|
|
|
Returns 1 - Var[y-ypred] / Var[y]
|
|
|
|
|
|
|
|
|
|
interpretation:
|
|
|
|
|
ev=0 => might as well have predicted zero
|
|
|
|
|
ev=1 => perfect prediction
|
|
|
|
|
ev<0 => worse than just predicting zero
|
|
|
|
|
|
|
|
|
|
:param y_pred: (np.ndarray) the prediction
|
|
|
|
|
:param y_true: (np.ndarray) the expected value
|
|
|
|
|
:return: (float) explained variance of ypred and y
|
|
|
|
|
"""
|
|
|
|
|
assert y_true.ndim == 1 and y_pred.ndim == 1
|
|
|
|
|
var_y = np.var(y_true)
|
|
|
|
|
return np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y
|
